Overview

Dataset statistics

Number of variables21
Number of observations23412
Missing cells145439
Missing cells (%)29.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 MiB
Average record size in memory168.0 B

Variable types

Categorical9
Numeric12

Alerts

Date has a high cardinality: 12401 distinct valuesHigh cardinality
Time has a high cardinality: 20472 distinct valuesHigh cardinality
ID has a high cardinality: 23412 distinct valuesHigh cardinality
Depth Error is highly overall correlated with Type and 1 other fieldsHigh correlation
Depth Seismic Stations is highly overall correlated with Magnitude Seismic Stations and 2 other fieldsHigh correlation
Magnitude Error is highly overall correlated with Magnitude Seismic Stations and 2 other fieldsHigh correlation
Magnitude Seismic Stations is highly overall correlated with Depth Seismic Stations and 2 other fieldsHigh correlation
Azimuthal Gap is highly overall correlated with Depth Seismic Stations and 2 other fieldsHigh correlation
Horizontal Distance is highly overall correlated with Horizontal Error and 1 other fieldsHigh correlation
Horizontal Error is highly overall correlated with Horizontal Distance and 2 other fieldsHigh correlation
Root Mean Square is highly overall correlated with StatusHigh correlation
Type is highly overall correlated with Depth Error and 3 other fieldsHigh correlation
Magnitude Type is highly overall correlated with Magnitude SourceHigh correlation
Source is highly overall correlated with Location Source and 2 other fieldsHigh correlation
Location Source is highly overall correlated with Source and 2 other fieldsHigh correlation
Magnitude Source is highly overall correlated with Magnitude Type and 3 other fieldsHigh correlation
Status is highly overall correlated with Depth Error and 10 other fieldsHigh correlation
Type is highly imbalanced (96.7%)Imbalance
Source is highly imbalanced (83.2%)Imbalance
Location Source is highly imbalanced (86.9%)Imbalance
Magnitude Source is highly imbalanced (60.0%)Imbalance
Depth Error has 18951 (80.9%) missing valuesMissing
Depth Seismic Stations has 16315 (69.7%) missing valuesMissing
Magnitude Error has 23085 (98.6%) missing valuesMissing
Magnitude Seismic Stations has 20848 (89.0%) missing valuesMissing
Azimuthal Gap has 16113 (68.8%) missing valuesMissing
Horizontal Distance has 21808 (93.1%) missing valuesMissing
Horizontal Error has 22256 (95.1%) missing valuesMissing
Root Mean Square has 6060 (25.9%) missing valuesMissing
Time is uniformly distributedUniform
ID is uniformly distributedUniform
ID has unique valuesUnique

Reproduction

Analysis started2023-03-23 18:15:12.174040
Analysis finished2023-03-23 18:15:27.366377
Duration15.19 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Date
Categorical

Distinct12401
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
03/11/2011
 
128
12/26/2004
 
51
02/27/2010
 
39
02/06/2013
 
27
11/15/2006
 
25
Other values (12396)
23142 

Length

Max length24
Median length10
Mean length10.001794
Min length10

Characters and Unicode

Total characters234162
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6326 ?
Unique (%)27.0%

Sample

1st row01/02/1965
2nd row01/04/1965
3rd row01/05/1965
4th row01/08/1965
5th row01/09/1965

Common Values

ValueCountFrequency (%)
03/11/2011 128
 
0.5%
12/26/2004 51
 
0.2%
02/27/2010 39
 
0.2%
02/06/2013 27
 
0.1%
11/15/2006 25
 
0.1%
11/16/2000 21
 
0.1%
03/12/2011 21
 
0.1%
02/04/1965 19
 
0.1%
07/18/1992 17
 
0.1%
12/03/1995 15
 
0.1%
Other values (12391) 23049
98.4%

Length

2023-03-24T00:15:27.423429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
03/11/2011 128
 
0.5%
12/26/2004 51
 
0.2%
02/27/2010 39
 
0.2%
02/06/2013 27
 
0.1%
11/15/2006 25
 
0.1%
11/16/2000 21
 
0.1%
03/12/2011 21
 
0.1%
02/04/1965 19
 
0.1%
07/18/1992 17
 
0.1%
12/03/1995 15
 
0.1%
Other values (12391) 23049
98.4%

Most occurring characters

ValueCountFrequency (%)
/ 46818
20.0%
0 44680
19.1%
1 41153
17.6%
9 25817
11.0%
2 24597
10.5%
8 10805
 
4.6%
7 10412
 
4.4%
6 8328
 
3.6%
3 7786
 
3.3%
5 7072
 
3.0%
Other values (6) 6694
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 187323
80.0%
Other Punctuation 46827
 
20.0%
Dash Punctuation 6
 
< 0.1%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44680
23.9%
1 41153
22.0%
9 25817
13.8%
2 24597
13.1%
8 10805
 
5.8%
7 10412
 
5.6%
6 8328
 
4.4%
3 7786
 
4.2%
5 7072
 
3.8%
4 6673
 
3.6%
Other Punctuation
ValueCountFrequency (%)
/ 46818
> 99.9%
: 6
 
< 0.1%
. 3
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
T 3
50.0%
Z 3
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 234156
> 99.9%
Latin 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 46818
20.0%
0 44680
19.1%
1 41153
17.6%
9 25817
11.0%
2 24597
10.5%
8 10805
 
4.6%
7 10412
 
4.4%
6 8328
 
3.6%
3 7786
 
3.3%
5 7072
 
3.0%
Other values (4) 6688
 
2.9%
Latin
ValueCountFrequency (%)
T 3
50.0%
Z 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 234162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 46818
20.0%
0 44680
19.1%
1 41153
17.6%
9 25817
11.0%
2 24597
10.5%
8 10805
 
4.6%
7 10412
 
4.4%
6 8328
 
3.6%
3 7786
 
3.3%
5 7072
 
3.0%
Other values (6) 6694
 
2.9%

Time
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct20472
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
02:56:58
 
5
14:09:03
 
5
02:21:11
 
4
13:48:32
 
4
06:29:16
 
4
Other values (20467)
23390 

Length

Max length24
Median length8
Mean length8.0020502
Min length8

Characters and Unicode

Total characters187344
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17806 ?
Unique (%)76.1%

Sample

1st row13:44:18
2nd row11:29:49
3rd row18:05:58
4th row18:49:43
5th row13:32:50

Common Values

ValueCountFrequency (%)
02:56:58 5
 
< 0.1%
14:09:03 5
 
< 0.1%
02:21:11 4
 
< 0.1%
13:48:32 4
 
< 0.1%
06:29:16 4
 
< 0.1%
05:11:35 4
 
< 0.1%
07:46:53 4
 
< 0.1%
14:57:12 4
 
< 0.1%
11:32:27 4
 
< 0.1%
15:56:35 4
 
< 0.1%
Other values (20462) 23370
99.8%

Length

2023-03-24T00:15:27.501501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
02:56:58 5
 
< 0.1%
14:09:03 5
 
< 0.1%
16:25:34 4
 
< 0.1%
15:06:45 4
 
< 0.1%
02:53:15 4
 
< 0.1%
11:57:34 4
 
< 0.1%
17:06:51 4
 
< 0.1%
07:44:11 4
 
< 0.1%
04:59:58 4
 
< 0.1%
16:28:15 4
 
< 0.1%
Other values (20462) 23370
99.8%

Most occurring characters

ValueCountFrequency (%)
: 46824
25.0%
1 25494
13.6%
0 25263
13.5%
2 18886
10.1%
3 15571
 
8.3%
5 14513
 
7.7%
4 14222
 
7.6%
6 6696
 
3.6%
9 6663
 
3.6%
8 6607
 
3.5%
Other values (5) 6605
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140505
75.0%
Other Punctuation 46827
 
25.0%
Dash Punctuation 6
 
< 0.1%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25494
18.1%
0 25263
18.0%
2 18886
13.4%
3 15571
11.1%
5 14513
10.3%
4 14222
10.1%
6 6696
 
4.8%
9 6663
 
4.7%
8 6607
 
4.7%
7 6590
 
4.7%
Other Punctuation
ValueCountFrequency (%)
: 46824
> 99.9%
. 3
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
T 3
50.0%
Z 3
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 187338
> 99.9%
Latin 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
: 46824
25.0%
1 25494
13.6%
0 25263
13.5%
2 18886
10.1%
3 15571
 
8.3%
5 14513
 
7.7%
4 14222
 
7.6%
6 6696
 
3.6%
9 6663
 
3.6%
8 6607
 
3.5%
Other values (3) 6599
 
3.5%
Latin
ValueCountFrequency (%)
T 3
50.0%
Z 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 187344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 46824
25.0%
1 25494
13.6%
0 25263
13.5%
2 18886
10.1%
3 15571
 
8.3%
5 14513
 
7.7%
4 14222
 
7.6%
6 6696
 
3.6%
9 6663
 
3.6%
8 6607
 
3.5%
Other values (5) 6605
 
3.5%

Latitude
Real number (ℝ)

Distinct20676
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6790331
Minimum-77.08
Maximum86.005
Zeros1
Zeros (%)< 0.1%
Negative12794
Negative (%)54.6%
Memory size183.0 KiB
2023-03-24T00:15:27.591583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-77.08
5-th percentile-53.346785
Q1-18.653
median-3.5685
Q326.19075
95-th percentile51.59545
Maximum86.005
Range163.085
Interquartile range (IQR)44.84375

Descriptive statistics

Standard deviation30.113183
Coefficient of variation (CV)17.934836
Kurtosis-0.60289206
Mean1.6790331
Median Absolute Deviation (MAD)18.8655
Skewness0.10180438
Sum39309.523
Variance906.80378
MonotonicityNot monotonic
2023-03-24T00:15:27.804777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.902 5
 
< 0.1%
51.5 5
 
< 0.1%
-5.589 5
 
< 0.1%
-5.605 5
 
< 0.1%
-21.126 4
 
< 0.1%
-6.323 4
 
< 0.1%
0.482 4
 
< 0.1%
-4.694 4
 
< 0.1%
36.429 4
 
< 0.1%
-4.651 4
 
< 0.1%
Other values (20666) 23368
99.8%
ValueCountFrequency (%)
-77.08 1
< 0.1%
-72.448 1
< 0.1%
-67.034 1
< 0.1%
-66.448 1
< 0.1%
-65.721 1
< 0.1%
-65.651 1
< 0.1%
-65.635 1
< 0.1%
-65.5984 1
< 0.1%
-65.592 1
< 0.1%
-65.552 1
< 0.1%
ValueCountFrequency (%)
86.005 1
< 0.1%
85.992 1
< 0.1%
85.735 1
< 0.1%
85.734 1
< 0.1%
85.686 1
< 0.1%
85.644 1
< 0.1%
85.632 1
< 0.1%
85.571 1
< 0.1%
85.263 1
< 0.1%
85.247 1
< 0.1%

Longitude
Real number (ℝ)

Distinct21474
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.639961
Minimum-179.997
Maximum179.998
Zeros0
Zeros (%)0.0%
Negative8665
Negative (%)37.0%
Memory size183.0 KiB
2023-03-24T00:15:27.908871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-179.997
5-th percentile-177.52845
Q1-76.34975
median103.982
Q3145.02625
95-th percentile168.9977
Maximum179.998
Range359.995
Interquartile range (IQR)221.376

Descriptive statistics

Standard deviation125.51196
Coefficient of variation (CV)3.1662988
Kurtosis-1.2498766
Mean39.639961
Median Absolute Deviation (MAD)59.963
Skewness-0.60064629
Sum928050.76
Variance15753.252
MonotonicityNot monotonic
2023-03-24T00:15:28.000955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.777 5
 
< 0.1%
167.34 4
 
< 0.1%
148.139 4
 
< 0.1%
154.934 4
 
< 0.1%
-174.922 4
 
< 0.1%
-179.361 4
 
< 0.1%
166.452 4
 
< 0.1%
-177.553 4
 
< 0.1%
142.75 4
 
< 0.1%
167.238 4
 
< 0.1%
Other values (21464) 23371
99.8%
ValueCountFrequency (%)
-179.997 1
< 0.1%
-179.996 1
< 0.1%
-179.993 1
< 0.1%
-179.991 1
< 0.1%
-179.989 1
< 0.1%
-179.987 1
< 0.1%
-179.985 1
< 0.1%
-179.984 1
< 0.1%
-179.983 1
< 0.1%
-179.977 1
< 0.1%
ValueCountFrequency (%)
179.998 2
< 0.1%
179.992 1
< 0.1%
179.989 1
< 0.1%
179.984 1
< 0.1%
179.981 1
< 0.1%
179.98 1
< 0.1%
179.978 1
< 0.1%
179.975 1
< 0.1%
179.963 1
< 0.1%
179.96 2
< 0.1%

Type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
Earthquake
23232 
Nuclear Explosion
 
175
Explosion
 
4
Rock Burst
 
1

Length

Max length17
Median length10
Mean length10.052153
Min length9

Characters and Unicode

Total characters235341
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowEarthquake
2nd rowEarthquake
3rd rowEarthquake
4th rowEarthquake
5th rowEarthquake

Common Values

ValueCountFrequency (%)
Earthquake 23232
99.2%
Nuclear Explosion 175
 
0.7%
Explosion 4
 
< 0.1%
Rock Burst 1
 
< 0.1%

Length

2023-03-24T00:15:28.092038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:15:28.188125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
earthquake 23232
98.5%
explosion 179
 
0.8%
nuclear 175
 
0.7%
rock 1
 
< 0.1%
burst 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 46639
19.8%
E 23411
9.9%
r 23408
9.9%
u 23408
9.9%
e 23407
9.9%
t 23233
9.9%
k 23233
9.9%
h 23232
9.9%
q 23232
9.9%
o 359
 
0.2%
Other values (11) 1779
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 211577
89.9%
Uppercase Letter 23588
 
10.0%
Space Separator 176
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 46639
22.0%
r 23408
11.1%
u 23408
11.1%
e 23407
11.1%
t 23233
11.0%
k 23233
11.0%
h 23232
11.0%
q 23232
11.0%
o 359
 
0.2%
l 354
 
0.2%
Other values (6) 1072
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
E 23411
99.2%
N 175
 
0.7%
R 1
 
< 0.1%
B 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
176
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 235165
99.9%
Common 176
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 46639
19.8%
E 23411
10.0%
r 23408
10.0%
u 23408
10.0%
e 23407
10.0%
t 23233
9.9%
k 23233
9.9%
h 23232
9.9%
q 23232
9.9%
o 359
 
0.2%
Other values (10) 1603
 
0.7%
Common
ValueCountFrequency (%)
176
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 235341
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 46639
19.8%
E 23411
9.9%
r 23408
9.9%
u 23408
9.9%
e 23407
9.9%
t 23233
9.9%
k 23233
9.9%
h 23232
9.9%
q 23232
9.9%
o 359
 
0.2%
Other values (11) 1779
 
0.8%

Depth
Real number (ℝ)

Distinct3485
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.767911
Minimum-1.1
Maximum700
Zeros170
Zeros (%)0.7%
Negative3
Negative (%)< 0.1%
Memory size183.0 KiB
2023-03-24T00:15:28.267197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1.1
5-th percentile10
Q114.5225
median33
Q354
95-th percentile386.835
Maximum700
Range701.1
Interquartile range (IQR)39.4775

Descriptive statistics

Standard deviation122.6519
Coefficient of variation (CV)1.733157
Kurtosis10.456851
Mean70.767911
Median Absolute Deviation (MAD)19
Skewness3.2906826
Sum1656818.3
Variance15043.488
MonotonicityNot monotonic
2023-03-24T00:15:28.363284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 3911
 
16.7%
33 3694
 
15.8%
35 592
 
2.5%
15 384
 
1.6%
20 336
 
1.4%
25 303
 
1.3%
30 302
 
1.3%
0 170
 
0.7%
45 139
 
0.6%
14 130
 
0.6%
Other values (3475) 13451
57.5%
ValueCountFrequency (%)
-1.1 1
 
< 0.1%
-0.097 1
 
< 0.1%
-0.076 1
 
< 0.1%
0 170
0.7%
0.02 1
 
< 0.1%
0.4 1
 
< 0.1%
0.7 1
 
< 0.1%
0.8 1
 
< 0.1%
0.9 4
 
< 0.1%
1 4
 
< 0.1%
ValueCountFrequency (%)
700 1
< 0.1%
691.6 1
< 0.1%
690 1
< 0.1%
688 1
< 0.1%
687.6 1
< 0.1%
682.2 1
< 0.1%
678.9 1
< 0.1%
677.4 1
< 0.1%
676.4 1
< 0.1%
675.6 1
< 0.1%

Depth Error
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct297
Distinct (%)6.7%
Missing18951
Missing (%)80.9%
Infinite0
Infinite (%)0.0%
Mean4.9931148
Minimum0
Maximum91.295
Zeros44
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-03-24T00:15:28.468380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5
Q11.8
median3.5
Q36.3
95-th percentile14.1
Maximum91.295
Range91.295
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation4.8751839
Coefficient of variation (CV)0.97638131
Kurtosis31.038774
Mean4.9931148
Median Absolute Deviation (MAD)1.7
Skewness3.7313617
Sum22274.285
Variance23.767418
MonotonicityNot monotonic
2023-03-24T00:15:28.564467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8 473
 
2.0%
1.7 440
 
1.9%
1.9 135
 
0.6%
3.5 85
 
0.4%
1.6 81
 
0.3%
3.3 76
 
0.3%
3 74
 
0.3%
3.2 74
 
0.3%
3.4 72
 
0.3%
3.1 72
 
0.3%
Other values (287) 2879
 
12.3%
(Missing) 18951
80.9%
ValueCountFrequency (%)
0 44
0.2%
0.015 1
 
< 0.1%
0.023 1
 
< 0.1%
0.06 1
 
< 0.1%
0.15 1
 
< 0.1%
0.155 1
 
< 0.1%
0.2 3
 
< 0.1%
0.21 2
 
< 0.1%
0.216 1
 
< 0.1%
0.24 1
 
< 0.1%
ValueCountFrequency (%)
91.295 1
 
< 0.1%
43.7 1
 
< 0.1%
40.6 1
 
< 0.1%
35.4 1
 
< 0.1%
35 1
 
< 0.1%
34.6 1
 
< 0.1%
33.4 1
 
< 0.1%
31.95 1
 
< 0.1%
31.61 25
0.1%
29.7 1
 
< 0.1%

Depth Seismic Stations
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct736
Distinct (%)10.4%
Missing16315
Missing (%)69.7%
Infinite0
Infinite (%)0.0%
Mean275.3641
Minimum0
Maximum934
Zeros29
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-03-24T00:15:28.660057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51
Q1146
median255
Q3384
95-th percentile570
Maximum934
Range934
Interquartile range (IQR)238

Descriptive statistics

Standard deviation162.14163
Coefficient of variation (CV)0.58882633
Kurtosis-0.099778406
Mean275.3641
Median Absolute Deviation (MAD)116
Skewness0.59878276
Sum1954259
Variance26289.909
MonotonicityNot monotonic
2023-03-24T00:15:28.756144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
0.1%
117 29
 
0.1%
169 26
 
0.1%
163 26
 
0.1%
209 25
 
0.1%
231 25
 
0.1%
198 25
 
0.1%
170 24
 
0.1%
167 24
 
0.1%
288 24
 
0.1%
Other values (726) 6840
29.2%
(Missing) 16315
69.7%
ValueCountFrequency (%)
0 29
0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 3
 
< 0.1%
10 5
 
< 0.1%
11 3
 
< 0.1%
12 2
 
< 0.1%
13 5
 
< 0.1%
ValueCountFrequency (%)
934 1
< 0.1%
929 1
< 0.1%
918 1
< 0.1%
885 1
< 0.1%
882 1
< 0.1%
862 1
< 0.1%
857 1
< 0.1%
832 1
< 0.1%
821 1
< 0.1%
814 1
< 0.1%

Magnitude
Real number (ℝ)

Distinct64
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8825308
Minimum5.5
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-03-24T00:15:28.855235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5.5
5-th percentile5.5
Q15.6
median5.7
Q36
95-th percentile6.7
Maximum9.1
Range3.6
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.42306564
Coefficient of variation (CV)0.071918985
Kurtosis4.3889221
Mean5.8825308
Median Absolute Deviation (MAD)0.2
Skewness1.8483457
Sum137721.81
Variance0.17898454
MonotonicityNot monotonic
2023-03-24T00:15:28.951323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.5 4685
20.0%
5.6 3967
16.9%
5.7 3079
13.2%
5.8 2346
10.0%
5.9 1947
8.3%
6 1580
 
6.7%
6.1 1213
 
5.2%
6.2 912
 
3.9%
6.3 765
 
3.3%
6.4 574
 
2.5%
Other values (54) 2344
10.0%
ValueCountFrequency (%)
5.5 4685
20.0%
5.51 1
 
< 0.1%
5.52 4
 
< 0.1%
5.53 1
 
< 0.1%
5.54 1
 
< 0.1%
5.55 1
 
< 0.1%
5.58 1
 
< 0.1%
5.6 3967
16.9%
5.62 1
 
< 0.1%
5.63 1
 
< 0.1%
ValueCountFrequency (%)
9.1 2
 
< 0.1%
8.8 1
 
< 0.1%
8.7 1
 
< 0.1%
8.6 2
 
< 0.1%
8.4 2
 
< 0.1%
8.3 5
 
< 0.1%
8.2 7
 
< 0.1%
8.1 7
 
< 0.1%
8 13
0.1%
7.9 21
0.1%

Magnitude Type
Categorical

Distinct10
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size183.0 KiB
MW
7722 
MWC
5669 
MB
3761 
MWB
2458 
MWW
1983 
Other values (5)
1816 

Length

Max length3
Median length2
Mean length2.4329959
Min length2

Characters and Unicode

Total characters56954
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMW
2nd rowMW
3rd rowMW
4th rowMW
5th rowMW

Common Values

ValueCountFrequency (%)
MW 7722
33.0%
MWC 5669
24.2%
MB 3761
16.1%
MWB 2458
 
10.5%
MWW 1983
 
8.5%
MS 1702
 
7.3%
ML 77
 
0.3%
MWR 26
 
0.1%
MD 6
 
< 0.1%
MH 5
 
< 0.1%
(Missing) 3
 
< 0.1%

Length

2023-03-24T00:15:29.042405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:15:29.131486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
mw 7722
33.0%
mwc 5669
24.2%
mb 3761
16.1%
mwb 2458
 
10.5%
mww 1983
 
8.5%
ms 1702
 
7.3%
ml 77
 
0.3%
mwr 26
 
0.1%
md 6
 
< 0.1%
mh 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M 23409
41.1%
W 19841
34.8%
B 6219
 
10.9%
C 5669
 
10.0%
S 1702
 
3.0%
L 77
 
0.1%
R 26
 
< 0.1%
D 6
 
< 0.1%
H 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 56954
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 23409
41.1%
W 19841
34.8%
B 6219
 
10.9%
C 5669
 
10.0%
S 1702
 
3.0%
L 77
 
0.1%
R 26
 
< 0.1%
D 6
 
< 0.1%
H 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 56954
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 23409
41.1%
W 19841
34.8%
B 6219
 
10.9%
C 5669
 
10.0%
S 1702
 
3.0%
L 77
 
0.1%
R 26
 
< 0.1%
D 6
 
< 0.1%
H 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56954
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 23409
41.1%
W 19841
34.8%
B 6219
 
10.9%
C 5669
 
10.0%
S 1702
 
3.0%
L 77
 
0.1%
R 26
 
< 0.1%
D 6
 
< 0.1%
H 5
 
< 0.1%

Magnitude Error
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)30.6%
Missing23085
Missing (%)98.6%
Infinite0
Infinite (%)0.0%
Mean0.071819572
Minimum0
Maximum0.41
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-03-24T00:15:29.234580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0323
Q10.046
median0.059
Q30.0755
95-th percentile0.1666
Maximum0.41
Range0.41
Interquartile range (IQR)0.0295

Descriptive statistics

Standard deviation0.051465944
Coefficient of variation (CV)0.71660054
Kurtosis14.114084
Mean0.071819572
Median Absolute Deviation (MAD)0.015
Skewness3.3799164
Sum23.485
Variance0.0026487434
MonotonicityNot monotonic
2023-03-24T00:15:29.330667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.048 18
 
0.1%
0.052 12
 
0.1%
0.063 10
 
< 0.1%
0.043 9
 
< 0.1%
0.054 9
 
< 0.1%
0.068 8
 
< 0.1%
0.05 8
 
< 0.1%
0.083 8
 
< 0.1%
0.075 8
 
< 0.1%
0.06 7
 
< 0.1%
Other values (90) 230
 
1.0%
(Missing) 23085
98.6%
ValueCountFrequency (%)
0 1
< 0.1%
0.02 2
< 0.1%
0.021 1
< 0.1%
0.025 1
< 0.1%
0.026 1
< 0.1%
0.027 2
< 0.1%
0.028 1
< 0.1%
0.029 2
< 0.1%
0.03 2
< 0.1%
0.031 1
< 0.1%
ValueCountFrequency (%)
0.41 1
< 0.1%
0.35 1
< 0.1%
0.341 1
< 0.1%
0.32 1
< 0.1%
0.302 1
< 0.1%
0.288 1
< 0.1%
0.26 1
< 0.1%
0.246 1
< 0.1%
0.245 1
< 0.1%
0.219 1
< 0.1%

Magnitude Seismic Stations
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct246
Distinct (%)9.6%
Missing20848
Missing (%)89.0%
Infinite0
Infinite (%)0.0%
Mean48.944618
Minimum0
Maximum821
Zeros47
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-03-24T00:15:29.430758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median28
Q366
95-th percentile165
Maximum821
Range821
Interquartile range (IQR)56

Descriptive statistics

Standard deviation62.943106
Coefficient of variation (CV)1.2860067
Kurtosis20.717227
Mean48.944618
Median Absolute Deviation (MAD)22
Skewness3.4371975
Sum125494
Variance3961.8346
MonotonicityNot monotonic
2023-03-24T00:15:29.638948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 127
 
0.5%
2 77
 
0.3%
3 70
 
0.3%
10 62
 
0.3%
6 56
 
0.2%
7 54
 
0.2%
4 54
 
0.2%
8 51
 
0.2%
0 47
 
0.2%
5 47
 
0.2%
Other values (236) 1919
 
8.2%
(Missing) 20848
89.0%
ValueCountFrequency (%)
0 47
 
0.2%
1 127
0.5%
2 77
0.3%
3 70
0.3%
4 54
0.2%
5 47
 
0.2%
6 56
0.2%
7 54
0.2%
8 51
0.2%
9 42
 
0.2%
ValueCountFrequency (%)
821 1
< 0.1%
621 1
< 0.1%
548 1
< 0.1%
526 1
< 0.1%
505 1
< 0.1%
484 1
< 0.1%
483 1
< 0.1%
428 1
< 0.1%
417 1
< 0.1%
410 1
< 0.1%

Azimuthal Gap
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1109
Distinct (%)15.2%
Missing16113
Missing (%)68.8%
Infinite0
Infinite (%)0.0%
Mean44.163532
Minimum0
Maximum360
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-03-24T00:15:29.733033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q124.1
median36
Q354
95-th percentile99.71
Maximum360
Range360
Interquartile range (IQR)29.9

Descriptive statistics

Standard deviation32.141486
Coefficient of variation (CV)0.72778341
Kurtosis17.595858
Mean44.163532
Median Absolute Deviation (MAD)13.9
Skewness3.2429041
Sum322349.62
Variance1033.0751
MonotonicityNot monotonic
2023-03-24T00:15:29.829120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 69
 
0.3%
17 64
 
0.3%
18 59
 
0.3%
21 55
 
0.2%
28 55
 
0.2%
22 53
 
0.2%
25 53
 
0.2%
24 52
 
0.2%
16 52
 
0.2%
27 50
 
0.2%
Other values (1099) 6737
28.8%
(Missing) 16113
68.8%
ValueCountFrequency (%)
0 2
 
< 0.1%
8 2
 
< 0.1%
8.4 1
 
< 0.1%
8.5 1
 
< 0.1%
8.7 1
 
< 0.1%
9 6
< 0.1%
9.3 1
 
< 0.1%
9.5 3
< 0.1%
9.6 1
 
< 0.1%
9.7 1
 
< 0.1%
ValueCountFrequency (%)
360 1
 
< 0.1%
335 1
 
< 0.1%
333 1
 
< 0.1%
328.5 1
 
< 0.1%
318 1
 
< 0.1%
313 1
 
< 0.1%
312 1
 
< 0.1%
296 3
< 0.1%
295 1
 
< 0.1%
287 1
 
< 0.1%

Horizontal Distance
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1448
Distinct (%)90.3%
Missing21808
Missing (%)93.1%
Infinite0
Infinite (%)0.0%
Mean3.9926598
Minimum0.004505
Maximum37.874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-03-24T00:15:29.932215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.004505
5-th percentile0.206745
Q10.96875
median2.3195
Q34.7245
95-th percentile14.6011
Maximum37.874
Range37.869495
Interquartile range (IQR)3.75575

Descriptive statistics

Standard deviation5.3772622
Coefficient of variation (CV)1.346787
Kurtosis11.8186
Mean3.9926598
Median Absolute Deviation (MAD)1.5705
Skewness3.1695663
Sum6404.2263
Variance28.914949
MonotonicityNot monotonic
2023-03-24T00:15:30.028302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.778 4
 
< 0.1%
1.806 3
 
< 0.1%
0.671 3
 
< 0.1%
0.828 3
 
< 0.1%
0.779 3
 
< 0.1%
3.056 3
 
< 0.1%
1.492 3
 
< 0.1%
1.753 3
 
< 0.1%
1.013 3
 
< 0.1%
0.681 3
 
< 0.1%
Other values (1438) 1573
 
6.7%
(Missing) 21808
93.1%
ValueCountFrequency (%)
0.004505 1
< 0.1%
0.005405 1
< 0.1%
0.008 1
< 0.1%
0.008108 1
< 0.1%
0.008296 1
< 0.1%
0.01 1
< 0.1%
0.01622 1
< 0.1%
0.026 1
< 0.1%
0.02613 1
< 0.1%
0.02703 1
< 0.1%
ValueCountFrequency (%)
37.874 1
< 0.1%
35.495 1
< 0.1%
35.377 1
< 0.1%
33.635 1
< 0.1%
33.259 1
< 0.1%
32.948 1
< 0.1%
32.49 1
< 0.1%
32.453 1
< 0.1%
32.416 1
< 0.1%
31.96 1
< 0.1%

Horizontal Error
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct186
Distinct (%)16.1%
Missing22256
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean7.6627587
Minimum0.085
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-03-24T00:15:30.132397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.085
5-th percentile1.3075
Q15.3
median6.7
Q38.1
95-th percentile10.8
Maximum99
Range98.915
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation10.430396
Coefficient of variation (CV)1.3611802
Kurtosis68.657024
Mean7.6627587
Median Absolute Deviation (MAD)1.4
Skewness8.1300784
Sum8858.149
Variance108.79315
MonotonicityNot monotonic
2023-03-24T00:15:30.229485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 29
 
0.1%
6.4 29
 
0.1%
7.5 28
 
0.1%
5.3 26
 
0.1%
7.3 26
 
0.1%
8 25
 
0.1%
6.7 25
 
0.1%
6.9 24
 
0.1%
6.2 23
 
0.1%
6 23
 
0.1%
Other values (176) 898
 
3.8%
(Missing) 22256
95.1%
ValueCountFrequency (%)
0.085 1
< 0.1%
0.11 2
< 0.1%
0.12 1
< 0.1%
0.13 1
< 0.1%
0.14 1
< 0.1%
0.15 1
< 0.1%
0.16 1
< 0.1%
0.19 1
< 0.1%
0.2 2
< 0.1%
0.21 1
< 0.1%
ValueCountFrequency (%)
99 14
0.1%
15.8 1
 
< 0.1%
15 1
 
< 0.1%
14.7 1
 
< 0.1%
14.6 1
 
< 0.1%
14.3 1
 
< 0.1%
13.9 2
 
< 0.1%
13.4 1
 
< 0.1%
13.3 2
 
< 0.1%
13.2 2
 
< 0.1%

Root Mean Square
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct190
Distinct (%)1.1%
Missing6060
Missing (%)25.9%
Infinite0
Infinite (%)0.0%
Mean1.022784
Minimum0
Maximum3.44
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size183.0 KiB
2023-03-24T00:15:30.326574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q10.9
median1
Q31.13
95-th percentile1.31
Maximum3.44
Range3.44
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.18854544
Coefficient of variation (CV)0.18434531
Kurtosis5.148365
Mean1.022784
Median Absolute Deviation (MAD)0.1
Skewness-0.033228797
Sum17747.348
Variance0.035549382
MonotonicityNot monotonic
2023-03-24T00:15:30.424663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1987
 
8.5%
1.1 1898
 
8.1%
0.9 1358
 
5.8%
1.2 1306
 
5.6%
1.3 716
 
3.1%
0.8 450
 
1.9%
1.4 290
 
1.2%
0.98 251
 
1.1%
0.89 251
 
1.1%
1.02 248
 
1.1%
Other values (180) 8597
36.7%
(Missing) 6060
25.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.005 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 5
< 0.1%
0.05 3
< 0.1%
0.06 6
< 0.1%
0.07 7
< 0.1%
0.08 6
< 0.1%
0.09 4
< 0.1%
0.092 1
 
< 0.1%
ValueCountFrequency (%)
3.44 1
 
< 0.1%
3.22 1
 
< 0.1%
2.75 1
 
< 0.1%
2.11 1
 
< 0.1%
2.1 1
 
< 0.1%
1.95 1
 
< 0.1%
1.89 1
 
< 0.1%
1.8 1
 
< 0.1%
1.78 1
 
< 0.1%
1.7 5
< 0.1%

ID
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct23412
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
ISCGEM860706
 
1
USP000AT1A
 
1
USP000ASVR
 
1
USP000ASVM
 
1
USP000ASTY
 
1
Other values (23407)
23407 

Length

Max length28
Median length10
Mean length10.242824
Min length7

Characters and Unicode

Total characters239805
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23412 ?
Unique (%)100.0%

Sample

1st rowISCGEM860706
2nd rowISCGEM860737
3rd rowISCGEM860762
4th rowISCGEM860856
5th rowISCGEM860890

Common Values

ValueCountFrequency (%)
ISCGEM860706 1
 
< 0.1%
USP000AT1A 1
 
< 0.1%
USP000ASVR 1
 
< 0.1%
USP000ASVM 1
 
< 0.1%
USP000ASTY 1
 
< 0.1%
USP000ASTG 1
 
< 0.1%
USP000ASTB 1
 
< 0.1%
USP000ASRN 1
 
< 0.1%
USP000ASQX 1
 
< 0.1%
USP000ASMB 1
 
< 0.1%
Other values (23402) 23402
> 99.9%

Length

2023-03-24T00:15:30.518749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
iscgem860706 1
 
< 0.1%
iscgem859231 1
 
< 0.1%
iscgem860856 1
 
< 0.1%
iscgem860890 1
 
< 0.1%
iscgem860922 1
 
< 0.1%
iscgem861007 1
 
< 0.1%
iscgem861111 1
 
< 0.1%
iscgemsup861125 1
 
< 0.1%
iscgem861148 1
 
< 0.1%
iscgem861155 1
 
< 0.1%
Other values (23402) 23402
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0 67896
28.3%
S 25287
 
10.5%
U 22612
 
9.4%
P 20831
 
8.7%
C 5750
 
2.4%
8 5704
 
2.4%
1 5522
 
2.3%
7 5422
 
2.3%
G 5338
 
2.2%
2 5291
 
2.2%
Other values (27) 70152
29.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 127872
53.3%
Decimal Number 111925
46.7%
Connector Punctuation 8
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 25287
19.8%
U 22612
17.7%
P 20831
16.3%
C 5750
 
4.5%
G 5338
 
4.2%
E 5075
 
4.0%
M 4492
 
3.5%
B 3105
 
2.4%
H 3011
 
2.4%
I 2866
 
2.2%
Other values (16) 29505
23.1%
Decimal Number
ValueCountFrequency (%)
0 67896
60.7%
8 5704
 
5.1%
1 5522
 
4.9%
7 5422
 
4.8%
2 5291
 
4.7%
3 4884
 
4.4%
5 4465
 
4.0%
4 4458
 
4.0%
6 4190
 
3.7%
9 4093
 
3.7%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 127872
53.3%
Common 111933
46.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 25287
19.8%
U 22612
17.7%
P 20831
16.3%
C 5750
 
4.5%
G 5338
 
4.2%
E 5075
 
4.0%
M 4492
 
3.5%
B 3105
 
2.4%
H 3011
 
2.4%
I 2866
 
2.2%
Other values (16) 29505
23.1%
Common
ValueCountFrequency (%)
0 67896
60.7%
8 5704
 
5.1%
1 5522
 
4.9%
7 5422
 
4.8%
2 5291
 
4.7%
3 4884
 
4.4%
5 4465
 
4.0%
4 4458
 
4.0%
6 4190
 
3.7%
9 4093
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 239805
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 67896
28.3%
S 25287
 
10.5%
U 22612
 
9.4%
P 20831
 
8.7%
C 5750
 
2.4%
8 5704
 
2.4%
1 5522
 
2.3%
7 5422
 
2.3%
G 5338
 
2.2%
2 5291
 
2.2%
Other values (27) 70152
29.3%

Source
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
US
20630 
ISCGEM
2460 
ISCGEMSUP
 
120
CI
 
61
GCMT
 
55
Other values (8)
 
86

Length

Max length9
Median length2
Mean length2.4633094
Min length2

Characters and Unicode

Total characters57671
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowISCGEM
2nd rowISCGEM
3rd rowISCGEM
4th rowISCGEM
5th rowISCGEM

Common Values

ValueCountFrequency (%)
US 20630
88.1%
ISCGEM 2460
 
10.5%
ISCGEMSUP 120
 
0.5%
CI 61
 
0.3%
GCMT 55
 
0.2%
NC 51
 
0.2%
AK 12
 
0.1%
OFFICIAL 8
 
< 0.1%
UW 6
 
< 0.1%
NN 4
 
< 0.1%
Other values (3) 5
 
< 0.1%

Length

2023-03-24T00:15:30.593817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us 20630
88.1%
iscgem 2460
 
10.5%
iscgemsup 120
 
0.5%
ci 61
 
0.3%
gcmt 55
 
0.2%
nc 51
 
0.2%
ak 12
 
0.1%
official 8
 
< 0.1%
uw 6
 
< 0.1%
nn 4
 
< 0.1%
Other values (3) 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 23334
40.5%
U 20756
36.0%
C 2755
 
4.8%
I 2657
 
4.6%
G 2635
 
4.6%
M 2635
 
4.6%
E 2581
 
4.5%
P 121
 
0.2%
N 59
 
0.1%
T 58
 
0.1%
Other values (7) 80
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 57671
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 23334
40.5%
U 20756
36.0%
C 2755
 
4.8%
I 2657
 
4.6%
G 2635
 
4.6%
M 2635
 
4.6%
E 2581
 
4.5%
P 121
 
0.2%
N 59
 
0.1%
T 58
 
0.1%
Other values (7) 80
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 57671
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 23334
40.5%
U 20756
36.0%
C 2755
 
4.8%
I 2657
 
4.6%
G 2635
 
4.6%
M 2635
 
4.6%
E 2581
 
4.5%
P 121
 
0.2%
N 59
 
0.1%
T 58
 
0.1%
Other values (7) 80
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 23334
40.5%
U 20756
36.0%
C 2755
 
4.8%
I 2657
 
4.6%
G 2635
 
4.6%
M 2635
 
4.6%
E 2581
 
4.5%
P 121
 
0.2%
N 59
 
0.1%
T 58
 
0.1%
Other values (7) 80
 
0.1%

Location Source
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct48
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
US
20350 
ISCGEM
2581 
CI
 
61
GCMT
 
56
NC
 
54
Other values (43)
 
310

Length

Max length6
Median length2
Mean length2.4596361
Min length1

Characters and Unicode

Total characters57585
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.1%

Sample

1st rowISCGEM
2nd rowISCGEM
3rd rowISCGEM
4th rowISCGEM
5th rowISCGEM

Common Values

ValueCountFrequency (%)
US 20350
86.9%
ISCGEM 2581
 
11.0%
CI 61
 
0.3%
GCMT 56
 
0.2%
NC 54
 
0.2%
GUC 46
 
0.2%
AEIC 40
 
0.2%
UNM 21
 
0.1%
PGC 19
 
0.1%
WEL 18
 
0.1%
Other values (38) 166
 
0.7%

Length

2023-03-24T00:15:30.670887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us 20350
86.9%
iscgem 2581
 
11.0%
ci 61
 
0.3%
gcmt 56
 
0.2%
nc 54
 
0.2%
guc 46
 
0.2%
aeic 40
 
0.2%
unm 21
 
0.1%
pgc 19
 
0.1%
wel 18
 
0.1%
Other values (38) 166
 
0.7%

Most occurring characters

ValueCountFrequency (%)
S 22991
39.9%
U 20432
35.5%
C 2869
 
5.0%
G 2722
 
4.7%
I 2705
 
4.7%
E 2673
 
4.6%
M 2672
 
4.6%
A 109
 
0.2%
T 91
 
0.2%
N 83
 
0.1%
Other values (12) 238
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 57581
> 99.9%
Connector Punctuation 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 22991
39.9%
U 20432
35.5%
C 2869
 
5.0%
G 2722
 
4.7%
I 2705
 
4.7%
E 2673
 
4.6%
M 2672
 
4.6%
A 109
 
0.2%
T 91
 
0.2%
N 83
 
0.1%
Other values (11) 234
 
0.4%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57581
> 99.9%
Common 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 22991
39.9%
U 20432
35.5%
C 2869
 
5.0%
G 2722
 
4.7%
I 2705
 
4.7%
E 2673
 
4.6%
M 2672
 
4.6%
A 109
 
0.2%
T 91
 
0.2%
N 83
 
0.1%
Other values (11) 234
 
0.4%
Common
ValueCountFrequency (%)
_ 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 22991
39.9%
U 20432
35.5%
C 2869
 
5.0%
G 2722
 
4.7%
I 2705
 
4.7%
E 2673
 
4.6%
M 2672
 
4.6%
A 109
 
0.2%
T 91
 
0.2%
N 83
 
0.1%
Other values (12) 238
 
0.4%

Magnitude Source
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
US
10458 
HRV
8223 
ISCGEM
2580 
GCMT
1489 
NC
 
533
Other values (19)
 
129

Length

Max length8
Median length7
Mean length2.9241415
Min length2

Characters and Unicode

Total characters68460
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowISCGEM
2nd rowISCGEM
3rd rowISCGEM
4th rowISCGEM
5th rowISCGEM

Common Values

ValueCountFrequency (%)
US 10458
44.7%
HRV 8223
35.1%
ISCGEM 2580
 
11.0%
GCMT 1489
 
6.4%
NC 533
 
2.3%
CI 61
 
0.3%
AK 12
 
0.1%
PAR 9
 
< 0.1%
OFFICIAL 8
 
< 0.1%
UW 6
 
< 0.1%
Other values (14) 33
 
0.1%

Length

2023-03-24T00:15:30.745955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us 10458
44.7%
hrv 8223
35.1%
iscgem 2580
 
11.0%
gcmt 1489
 
6.4%
nc 533
 
2.3%
ci 61
 
0.3%
ak 12
 
0.1%
par 9
 
< 0.1%
official 8
 
< 0.1%
uw 6
 
< 0.1%
Other values (14) 33
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 13046
19.1%
U 10474
15.3%
R 8235
12.0%
H 8223
12.0%
V 8223
12.0%
C 4683
 
6.8%
G 4081
 
6.0%
M 4072
 
5.9%
I 2661
 
3.9%
E 2585
 
3.8%
Other values (17) 2177
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 68432
> 99.9%
Decimal Number 24
 
< 0.1%
Connector Punctuation 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 13046
19.1%
U 10474
15.3%
R 8235
12.0%
H 8223
12.0%
V 8223
12.0%
C 4683
 
6.8%
G 4081
 
6.0%
M 4072
 
6.0%
I 2661
 
3.9%
E 2585
 
3.8%
Other values (11) 2149
 
3.1%
Decimal Number
ValueCountFrequency (%)
0 14
58.3%
1 6
25.0%
2 2
 
8.3%
3 1
 
4.2%
9 1
 
4.2%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 68432
> 99.9%
Common 28
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 13046
19.1%
U 10474
15.3%
R 8235
12.0%
H 8223
12.0%
V 8223
12.0%
C 4683
 
6.8%
G 4081
 
6.0%
M 4072
 
6.0%
I 2661
 
3.9%
E 2585
 
3.8%
Other values (11) 2149
 
3.1%
Common
ValueCountFrequency (%)
0 14
50.0%
1 6
21.4%
_ 4
 
14.3%
2 2
 
7.1%
3 1
 
3.6%
9 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 13046
19.1%
U 10474
15.3%
R 8235
12.0%
H 8223
12.0%
V 8223
12.0%
C 4683
 
6.8%
G 4081
 
6.0%
M 4072
 
5.9%
I 2661
 
3.9%
E 2585
 
3.8%
Other values (17) 2177
 
3.2%

Status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size183.0 KiB
Reviewed
20773 
Automatic
2639 

Length

Max length9
Median length8
Mean length8.11272
Min length8

Characters and Unicode

Total characters189935
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutomatic
2nd rowAutomatic
3rd rowAutomatic
4th rowAutomatic
5th rowAutomatic

Common Values

ValueCountFrequency (%)
Reviewed 20773
88.7%
Automatic 2639
 
11.3%

Length

2023-03-24T00:15:30.820023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:15:30.890086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
reviewed 20773
88.7%
automatic 2639
 
11.3%

Most occurring characters

ValueCountFrequency (%)
e 62319
32.8%
i 23412
 
12.3%
R 20773
 
10.9%
v 20773
 
10.9%
w 20773
 
10.9%
d 20773
 
10.9%
t 5278
 
2.8%
A 2639
 
1.4%
u 2639
 
1.4%
o 2639
 
1.4%
Other values (3) 7917
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 166523
87.7%
Uppercase Letter 23412
 
12.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 62319
37.4%
i 23412
 
14.1%
v 20773
 
12.5%
w 20773
 
12.5%
d 20773
 
12.5%
t 5278
 
3.2%
u 2639
 
1.6%
o 2639
 
1.6%
m 2639
 
1.6%
a 2639
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
R 20773
88.7%
A 2639
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 189935
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 62319
32.8%
i 23412
 
12.3%
R 20773
 
10.9%
v 20773
 
10.9%
w 20773
 
10.9%
d 20773
 
10.9%
t 5278
 
2.8%
A 2639
 
1.4%
u 2639
 
1.4%
o 2639
 
1.4%
Other values (3) 7917
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 62319
32.8%
i 23412
 
12.3%
R 20773
 
10.9%
v 20773
 
10.9%
w 20773
 
10.9%
d 20773
 
10.9%
t 5278
 
2.8%
A 2639
 
1.4%
u 2639
 
1.4%
o 2639
 
1.4%
Other values (3) 7917
 
4.2%

Interactions

2023-03-24T00:15:25.579249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:14.031731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:15.235826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:16.272770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:17.326728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:18.423726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:19.391109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:20.546664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:21.404444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:22.371330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:23.520376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:24.511277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:25.674335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:14.152841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:15.329911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:16.368856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:17.413807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:18.511806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:19.488198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:20.619730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:21.488521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:22.463414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:23.605453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:24.592351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:25.765418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:14.248928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:15.419993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:16.459940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:17.499885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:18.594882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:19.579280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:20.691797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:21.570596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:22.665599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:23.691532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:24.674426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:25.854499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:14.340011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:15.508074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:16.548020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:17.582961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:18.679959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:19.667361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:20.763862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:21.654672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:22.753679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:23.777610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:24.756501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:25.938079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:14.422086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:15.588147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:16.629093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:17.774135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:18.758029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:19.749435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:20.836928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:21.737747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:22.835753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:23.861686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:24.837574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:26.024157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:14.511167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:15.673224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:16.716173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:17.850204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:18.836101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:19.836514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:20.897984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:21.823329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:22.918828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:23.933752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:24.909639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:26.118242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:14.604251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:15.761305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:16.804252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:17.931278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:18.921178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:19.925596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:20.972051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:21.901904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:23.007910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:24.017829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:25.101815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:26.195312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:14.691331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:15.833369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:16.878320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:18.002343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:18.983235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:19.998662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:21.040113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:21.969965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:23.080976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:24.088893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:25.167875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:26.279389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:14.775407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:15.915444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:16.962397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:18.085418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:19.069314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:20.076733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:21.108175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:22.048037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:23.168055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:24.161960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:25.236938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:26.367469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:14.865489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:16.003525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:17.049476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:18.169494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:19.154390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:20.286427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:21.183243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:22.135116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:23.256136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:24.249039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:25.322015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:26.457551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:14.953569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:16.091604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:17.138557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:18.255573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:19.225455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:20.372506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:21.259312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:22.212186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:23.345216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:24.339120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:25.408093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:26.545631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:15.040648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:16.178684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:17.230641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:18.337648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:19.298521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:20.457582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:21.326373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:22.283250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:23.430294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:24.422196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-24T00:15:25.488166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-03-24T00:15:30.960149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
LatitudeLongitudeDepthDepth ErrorDepth Seismic StationsMagnitudeMagnitude ErrorMagnitude Seismic StationsAzimuthal GapHorizontal DistanceHorizontal ErrorRoot Mean SquareTypeMagnitude TypeSourceLocation SourceMagnitude SourceStatus
Latitude1.0000.157-0.0270.0080.3920.034-0.2160.309-0.043-0.346-0.378-0.1890.1250.0550.0790.1380.0770.105
Longitude0.1571.0000.0820.1250.0070.041-0.1060.130-0.2230.0050.022-0.0430.1200.1000.1270.1800.1070.062
Depth-0.0270.0821.0000.2190.221-0.039-0.1770.062-0.234-0.1250.012-0.1200.0170.0560.0300.0000.0000.017
Depth Error0.0080.1250.2191.000-0.244-0.2290.175-0.0200.155-0.0480.1540.1640.5830.1930.2880.2850.2961.000
Depth Seismic Stations0.3920.0070.221-0.2441.0000.418-0.2240.850-0.504-0.113-0.459-0.2590.0690.1520.1070.1180.1251.000
Magnitude0.0340.041-0.039-0.2290.4181.0000.029-0.002-0.355-0.081-0.1370.0740.0200.0790.3350.0180.3450.096
Magnitude Error-0.216-0.106-0.1770.175-0.2240.0291.000-0.7100.3550.0220.2860.0380.6110.4380.4640.3150.4641.000
Magnitude Seismic Stations0.3090.1300.062-0.0200.850-0.002-0.7101.000-0.4560.2460.365-0.2710.1980.0850.0000.0530.0001.000
Azimuthal Gap-0.043-0.223-0.2340.155-0.504-0.3550.355-0.4561.000-0.115-0.0250.0990.5330.3020.2730.2520.2631.000
Horizontal Distance-0.3460.005-0.125-0.048-0.113-0.0810.0220.246-0.1151.0000.6650.0270.0000.1100.0000.0000.0761.000
Horizontal Error-0.3780.0220.0120.154-0.459-0.1370.2860.365-0.0250.6651.0000.0880.9240.4070.4680.4620.4961.000
Root Mean Square-0.189-0.043-0.1200.164-0.2590.0740.038-0.2710.0990.0270.0881.0000.0760.2520.3290.3620.2381.000
Type0.1250.1200.0170.5830.0690.0200.6110.1980.5330.0000.9240.0761.0000.1210.0630.0500.0770.029
Magnitude Type0.0550.1000.0560.1930.1520.0790.4380.0850.3020.1100.4070.2520.1211.0000.4590.4610.5920.492
Source0.0790.1270.0300.2880.1070.3350.4640.0000.2730.0000.4680.3290.0630.4591.0000.9020.8721.000
Location Source0.1380.1800.0000.2850.1180.0180.3150.0530.2520.0000.4620.3620.0500.4610.9021.0000.6160.999
Magnitude Source0.0770.1070.0000.2960.1250.3450.4640.0000.2630.0760.4960.2380.0770.5920.8720.6161.0000.988
Status0.1050.0620.0171.0001.0000.0961.0001.0001.0001.0001.0001.0000.0290.4921.0000.9990.9881.000

Missing values

2023-03-24T00:15:26.711782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-24T00:15:26.960008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-24T00:15:27.226250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateTimeLatitudeLongitudeTypeDepthDepth ErrorDepth Seismic StationsMagnitudeMagnitude TypeMagnitude ErrorMagnitude Seismic StationsAzimuthal GapHorizontal DistanceHorizontal ErrorRoot Mean SquareIDSourceLocation SourceMagnitude SourceStatus
001/02/196513:44:1819.246145.616Earthquake131.6NaNNaN6.0MWNaNNaNNaNNaNNaNNaNISCGEM860706ISCGEMISCGEMISCGEMAutomatic
101/04/196511:29:491.863127.352Earthquake80.0NaNNaN5.8MWNaNNaNNaNNaNNaNNaNISCGEM860737ISCGEMISCGEMISCGEMAutomatic
201/05/196518:05:58-20.579-173.972Earthquake20.0NaNNaN6.2MWNaNNaNNaNNaNNaNNaNISCGEM860762ISCGEMISCGEMISCGEMAutomatic
301/08/196518:49:43-59.076-23.557Earthquake15.0NaNNaN5.8MWNaNNaNNaNNaNNaNNaNISCGEM860856ISCGEMISCGEMISCGEMAutomatic
401/09/196513:32:5011.938126.427Earthquake15.0NaNNaN5.8MWNaNNaNNaNNaNNaNNaNISCGEM860890ISCGEMISCGEMISCGEMAutomatic
501/10/196513:36:32-13.405166.629Earthquake35.0NaNNaN6.7MWNaNNaNNaNNaNNaNNaNISCGEM860922ISCGEMISCGEMISCGEMAutomatic
601/12/196513:32:2527.35787.867Earthquake20.0NaNNaN5.9MWNaNNaNNaNNaNNaNNaNISCGEM861007ISCGEMISCGEMISCGEMAutomatic
701/15/196523:17:42-13.309166.212Earthquake35.0NaNNaN6.0MWNaNNaNNaNNaNNaNNaNISCGEM861111ISCGEMISCGEMISCGEMAutomatic
801/16/196511:32:37-56.452-27.043Earthquake95.0NaNNaN6.0MWNaNNaNNaNNaNNaNNaNISCGEMSUP861125ISCGEMSUPISCGEMISCGEMAutomatic
901/17/196510:43:17-24.563178.487Earthquake565.0NaNNaN5.8MWNaNNaNNaNNaNNaNNaNISCGEM861148ISCGEMISCGEMISCGEMAutomatic
DateTimeLatitudeLongitudeTypeDepthDepth ErrorDepth Seismic StationsMagnitudeMagnitude TypeMagnitude ErrorMagnitude Seismic StationsAzimuthal GapHorizontal DistanceHorizontal ErrorRoot Mean SquareIDSourceLocation SourceMagnitude SourceStatus
2340212/24/201603:58:55-5.1460153.5166Earthquake30.001.8NaN5.8MWWNaNNaN14.001.6487.00.8500US10007MFPUSUSUSReviewed
2340312/25/201614:22:27-43.4029-73.9395Earthquake38.001.9NaN7.6MWWNaNNaN29.000.3516.80.8000US10007MN3USUSUSReviewed
2340412/25/201614:32:13-43.4810-74.4771Earthquake14.933.3NaN5.6MB0.06783.096.000.6977.10.5200US10007MNBUSUSUSReviewed
2340512/27/201623:20:5645.719226.5230Earthquake97.001.8NaN5.6MWWNaNNaN14.000.4655.10.7800US10007N3RUSUSUSReviewed
2340612/28/201608:18:0138.3754-118.8977Earthquake10.801.334.05.6ML0.35020.035.860.132NaN0.1988NN00570709NNNNNNReviewed
2340712/28/201608:22:1238.3917-118.8941Earthquake12.301.240.05.6ML0.32018.042.470.120NaN0.1898NN00570710NNNNNNReviewed
2340812/28/201609:13:4738.3777-118.8957Earthquake8.802.033.05.5ML0.26018.048.580.129NaN0.2187NN00570744NNNNNNReviewed
2340912/28/201612:38:5136.9179140.4262Earthquake10.001.8NaN5.9MWWNaNNaN91.000.9924.81.5200US10007NAFUSUSUSReviewed
2341012/29/201622:30:19-9.0283118.6639Earthquake79.001.8NaN6.3MWWNaNNaN26.003.5536.01.4300US10007NL0USUSUSReviewed
2341112/30/201620:08:2837.3973141.4103Earthquake11.942.2NaN5.5MB0.029428.097.000.6814.50.9100US10007NTDUSUSUSReviewed